The Need for a Danube Post-Covid 19 Strategy for Economic Survival

Romeo-Victor Ionescu¹

Abstract: EU27 is facing to a new crisis generated by the Covid-19 pandemic. This crisis is different from the previous ones because it achieved a maximum degree of complexity. The specialists point out as components of this crisis: social, political, medical and economic elements. As a result, EU27 is expected to decrease its GDP growth rate by 7.4% in 2020 following the outbreak of the Covid-19 (Clark, D., 2020). A forecasted economic recovery in 2021 will be not able to balance the present economic recession in the EU27.

Keywords:

1. Introduction

EU27 is facing to a new crisis generated by the Covid-19 pandemic. This crisis is different from the previous ones because it achieved a maximum degree of complexity. The specialists point out as components of this crisis: social, political, medical and economic elements. As a result, EU27 is expected to decrease its GDP growth rate by 7.4% in 2020 following the outbreak of the Covid-19 (Clark, D., 2020). A forecasted economic recovery in 2021 will be not able to balance the present economic recession in the EU27 (see Figure 1).

Figure 1. Real GDP growth rate forecast (%)

According to Figure 1, the forecasted economic recovery in 2021 will be not enough to cover the decrease from 2020.

The EU Danube Member States are facing to the same situation connected to the pandemic’s impact in 2020. The effects of the pandemic on these economies varies a lot. The decrease in real GDP in 2020 achieves 9.1% in Croatia, compared to 5.5% in Austria (see Figure 2).

Figure 2. Real GDP Growth Rate Forecast in EU Danube States (%)

The present research deals to the idea that the economic recession in the EU Danube States would lead to a common position in passing recession and why not a common economic strategy able to valorise the advantages which come from their status as Danube states.

In order to support the above approach, the present paper proposes a model of maximization the national economic output in order to obtain maximum of economic efficiency under the pandemic’s impact.

The objective of the research are:

O1: Identifying the elements which are essential in improving the national economic recovery capacity during the present crisis.

O2: Quantifying each element of influence and building an adequate database.

O3: Realising a hierarchy of the most important regional actors according to their economic recovery capacity.

2. Literature Review

Evan that the present pandemic crises is acting now, there are a lot of scientific researches which are trying to explain and to quantify the pandemic’s impact on the economic development. Some of the most representative such papers are presented in Table 1.

Table 1. Literature Review.

The above short literature review supports the need of a new model able to quantify and to improve the EU Danube States’ economic recovery capacity.

3. Methods and Methodology

The present analysis takes into consideration six important indicators which have maximum impact in the national defence capacity against the complex crisis in 2020 and afterwards: GDP (PPP)/capita, net export, hospital beds/1000 inhabitants, employment, and general government gross debt. The whole approach is based on the following hypotheses:

H1: Some from above indicators (GDP (PPP)/capita, net export, hospital beds/1000 inhabitants and employment have to be as large as possible.

H2: The general government gross debt has to be positive as less as possible, because it quantifies the dependence of the economic entities by the loans.

H3: In order to simplify the analysis, it is assumed that all above five indicators have the same contribution to the national/regional recovery capacity against the present complex crisis.

According to the above presentation, the propose model is basically a maximization function as:

(1)

If: (2)

and ε ≠ 0. (3)

In the above equation: gdp - GDP/capita; nexp – net export; hb - Hospital beds/1000 inhabitants; L - employment; ggd - general government gross debt; i – all seven EU Danube States; - correction element; a, b, c, d, e, f - coefficients that express the weight of each indicator in achieving the ability to to achieve the economic recovery. According to H3:

a = b = c =d = e (4)

In order to start the analysis, a dedicated database was built using the latest official statistical data (European Commission, 2020). The data used by this analysis cover 2011-2020 (see Appendix). The database covers 10 years and is representative for a correct analysis. The statistical official data were completed with ARIMA forecasting procedure, using IBM-SPSS25 software.

According to the data from the Appendix, the socio-economic and medical performances in 2020 are worst compared to the previous year. The implementation of the model for 2020 leads to the following results:

Y = (51109 + 26088 + 51410 + 34771 + 31070 + 22807 + 24870)*0.2+ (14.6 - 6.0 + 232.1+ 1.8 + 5.7 + 1.4-0.4)*0.2+ (722.7+719.2+781.3+562.9+691.0+801.3+530.1)*0.2+ (4090+8324+40387+2579+4592+3114+1620)*0.2 - (300.1+76.7+2346.7+49.6+90.7+12.7+43.0)*0.2= 48425+ 49.84+ 961.7+ 12941.2- 583.9= 61793.84

The value of the total impact of the Covid - 19 pandemic on the Danube EU Members State has to be compared to forecasted one for 2021. In order to do this, a forecasting procedure under IBM-SPSS 25, which uses ARIMA conditions, will be applied.

4. Results and Discussion

The forecasted data for GDP/capita in 2021 are presented in Figure 3.

Figure 3. The Forecasted data for GDP/Capita in 2021 (USD)

According to Table 2, all Danube EU Member States will face to a drop in their economic development in 2020. It will be followed by a relative economic recovery in 2021. Unfortunately, this last recovery will be not enough to cover the drop in 2020. The most official optimistic approaches talk about a complete recover only in 2020, if the pandemic will not develop under a new wave.

The second forecasted indicator is Net export of goods. It represents the difference between the value of the exported goods and the value of the imported goods in a year (see Figure 4).

Figure 4. The Forecasted Data for Net Export of Goods in 2021 (bn. Euros).

Some analysed countries (like Austria and Germany) continued to obtain advantage from the international trade of goods. These advantage can be used in order to improve their fight against pandemic and economic recession. Slovakia, Bulgaria and Hungary will maintain their terms of their trade balances for goods, while Romania and Croatia will face to a worse situation in 2021.

An important element in fighting against the present pandemic is the number of the hospital beds. As general trend, this number decreased during the analysed period. The evolution of this indicator in 2021 is presented in Figure 5.

Figure 5. The Forecasted Data for Hospital Beds/1000 Inhabitants in 2021.

According to Figure 5, only two Danube countries will succeed in increasing their number of the hospital beds/100000 inhabitants. This means that the other five states will be not able to face to a new wave of Covid-19 pandemic.

The employment represents an important element in supporting the economic recovery. The trend of this indicator in the Danube EU Member States is presented in Figure 6.

Figure 6. The Forecasted data for Employment in 2021 (Thousands Persons)

It is no doubt that all Danube EU Members States will force the employment increase in 2021 compared to the previous year, in order to produce more and to increase all the revenues.

Finally, the general government gross debt potentiates the economy’s capacity to fight against the Covid-19 impact and economic recession, as well. The statistical data regarding this indicator in 2021 are presented in Figure 7.

Figure 7. The Forecasted Data for the General Government Gross Debt in 2021 (bn. Euros).

As a general point of view, the states which will be able to decrease their gross debt will obtain more instruments in fighting against Covid-19 pandemic and recession.

According to the data quantified for 2021, the implementation of the proposed model lead to the following values:

Y = (53664 + 27184 + 54443 + 37066 + 32934 + 24175 + 26735) * 0.2+ (14.7 - 6.4 + 246.7+ 1.8 + 5.7 + 1.4 - 0.5) * 0.2+ (717.2+727.6+775.6+559.3+689.2+818.3+521.9) * 0.2+ (4147+8374+40629+2631+4643+3148+1669) * 0.2 - (291.1+83.2+2257.5+49.8+89.3+12.7+40.8) * 0.2= 51240.2 + 52.68 + 961.82 + 13048.2 – 564.88 = 64738.02

5. Conclusions

The above analysis leads to a single conclusion: the impact of the pandemic is a very important one and the economic restart will be more than difficult. The economic development in the EU Danube Member States will lead to great disparities. This disparities will be not eliminated in 2021 if the present trend will continue. In order to decrease the socio-economic disparities, to improve the labour market mechanisms and to ensure a better health care protection in this region, the solution would be a common strategy under the future German EU presidency. Unfortunately, the official statements of the German government pointed out the idea of a policy focused on the international relationship with China not to a consolidation of the economic region.

The theoretical model proposed in this paper was practically tested by case analysis on all EU Danube Member States, highlighting the validity of the model through statistical tests run through IBM-SPSS 25 software. In addition, the model is obviously useful for the regional / local decision-makers in order to mitigate the described above phenomena. From a theoretical point of view, the model is a useful exercise in universities and not only, but especially for entrepreneurs focused on developing their business across the Danube region. From the research point of view, the model is interesting because it brings a new approach in the field and offers possibilities for further extension of the analysis by increasing the number of variables taken into account.

The limitations of the study are related to the limited nature of the used data and the introduced variables during the conceptualization of the model. These can be further extended to adapt the model to the current crisis situation.

For our point of view, we will continue the research in order to apply this model to the same Danube region in the context of the new developments related to a new Covid-19 wave. As a result, the coefficients which express the weight of each indicator in achieving the ability to achieve the economic recovery and to fight against the pandemic (a, b, c, d, e) have to be changed.

References

Bennedsen, M.; Larsen, B.; Schmutte, I. & Scur, D. (2020). Preserving job matches during the COVID-19 pandemic: Firm-level evidence on the role of government aid. Covid Economics Vetted and Real-Time Papers. Issue 27, pp. 1-30.

Clark, D. (2020). GDP growth rate forecasts in Europe 2020-2021. Retrieved from https://www.statista.com/statistics/1102546/coronavirus-european-gdp-growth, date: 10.06.2020.

European Commission (2020). European Economic Forecast Spring. European Economy. No. 125, pp. 83, 109, 115, 121, 129, 133.

Eurostat (2020). Hospital beds/100000 inhabitants. Retrieved from: https://ec.europa.eu/eurostat/tgm/table.do?tab=table&init=1&language=en&pcode=tps00046&plugin=1, date: 12.06.2020.

Eurostat (2020b). Employment and activity by sex and age - annual data. Retrieved from: https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=lfsi_emp_a&lang=en, date: 10.06.2020.

Goodell, J.W. (2020). COVID-19 and finance: Agendas for future research. Finance Research Letters. pp. 1-5, https://doi.org/10.1016/j.frl.2020.101512.

Haleem, A.; Javaid, M.; Vaishya, R. & Deshmukh, S.G. (2020). Areas of academic research with the impact of COVID-19. Elsevier Public Health Emergency Collection. PMC7158762, doi: 10.1016/j.ajem.2020.04.022

IMF WEO (2019). GDP (PPP) per capita - international dollars. Retrieved from: https://www.imf.org/external/pubs/ft/weo/2018/02/weodata/weorept.aspx?pr.x=45&pr.y=2&sy=2019&ey=2019&scsm=1&ssd=1&sort=country&ds=.&br=1&c=914%2C946%2C137%2C962%2C122%2C181%2C913%2C124%2C921%2C943%2C963%2C918%2C138%2C142%2C964%2C182%2C960%2C423%2C935%2C968%2C128%2C922%2C942%2C939%2C936%2C961%2C172%2C132%2C184%2C134%2C174%2C144%2C146%2C944%2C176%2C178%2C136%2C926%2C112%2C967%2C941&s=PPPPC&grp=0&a= , date: 12.06.2020.

Maliszewska, M.; Mattoo, A. & van der Mensbrugghe, D. (2020). The Potential Impact of COVID-19 on GDP and Trade. World Bank Policy Research Working Paper 9211, pp. 1-26.

Peterson, K. O. & Thankom, G. A. (2020). Spillover of COVID-19: impact on the Global Economy. SSRN Electronic Journal. DOI: 10.2139/ssrn.3562570, pp. 1-27.

Appendix

Table 2. Statistical Database